In recent years, interest in applications of organic materials in electronic devices (light
emitting diodes, field effect transistors, solar cells), has increased rapidly. The advantages of
organic materials are the ease of processing, lower production costs and structural flexibility
allowing achievement of the desired electrical and mechanical characteristics. Very recently,
there have been demonstrations of blends of polymer and metal nanoparticles and/or small
organic molecules in memory devices; such memory devices are called polymer memory devices
(PMDs). These devices show two electrical conductance states (“high” and “low”) when voltage
is applied, thus rendering the structures suitable for data retention.
These two states can be viewed as the realisation of non-volatile electrical memory. There is
always growing need to look for inexpensive, fast, high-density memory devices with longer
retention times and PMDs do possess some of these aforesaid criteria. Albeit, there is a rapid
development in this area, the memory mechanism is still unclear. This work attempts to analyse
the memory effect in PMDs and proposes a theory based on experimental data. The thin film
polymer blends (polyvinyl acetate, polyvinyl alcohol and polystyrene) and small organic
molecules were deposited by spin coating onto a glass substrate marked with thin metal tracks. A
top contact was evaporated onto the blend after drying - this resulted in a metal-organic-metal
(MOM) structure. MOM devices with different metal electrodes (a series of metals with different
work functions Al, In,Cu,Cr, Ag and Au) were used to understand the exact electrical transport
mechanism through the blend and the individual polymers. An in-depth electrical analysis of
these MOM devices was carried out using an HP4140B picoammeter (current-voltage) and an
LCR HP4192 bridge. FTIR and UV-VIS spectroscopy were also conducted in order to
understand blend properties and the effect of the same, if any, on the electrical charging
mechanism in the PMDs.

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The authors would like to thank EPSRC (Grant # EP/E047785/1) for supporting this work.